CN114460227A

CN114460227A - Electrolyte abnormity monitoring method and system

Info

Publication number: CN114460227A
Application number: CN202210034622.0A
Authority: CN
Inventors: 林建平; 胡夏斌; 林建灶; 叶栋; 徐关峰
Original assignee: Hangzhou Sanal Environmental Technology Co ltd
Current assignee: Hangzhou Sanal Environmental Technology Co ltd
Priority date: 2022-01-12
Filing date: 2022-01-12
Publication date: 2022-05-10
Anticipated expiration: 2042-01-12
Also published as: CN114460227B; CN116466023A

Abstract

The embodiment of the specification provides an electrolyte abnormity monitoring method, which comprises the following steps: obtaining electrolyte state information, wherein the electrolyte state information comprises at least one of electrolyte temperature information, electrolyte flow information, current density information, electrolyte components, electrolyte impurities and cathode plate surface conditions; judging whether the electrolyte state is abnormal or not based on the electrolyte state information; in response to the electrolyte condition anomaly, a treatment protocol is determined.

Description

Electrolyte abnormity monitoring method and system

Description of the cases

The application is a divisional application proposed by Chinese application with application date of 2022, month 01 and 12, application number of 202210030282.4 and title of "electrolyte abnormity monitoring method and system".

Technical Field

The specification relates to the field of electrolytic production, in particular to an electrolyte abnormity monitoring method and system.

Background

In the process of electrolytic production, if the electrolyte is abnormal, the electrolytic production quality is reduced, the production efficiency is reduced, and the energy consumption is increased. The state of the electrolyte is mostly checked and processed manually, so that abnormal conditions are difficult to be solved quickly and efficiently, and the efficiency and the quality of electrolytic production are reduced.

Therefore, it is desirable to provide an electrolyte abnormality monitoring method and system, which can monitor the electrolyte state information, find the electrolyte abnormal state in time and conveniently, and process the electrolyte abnormal state.

Disclosure of Invention

One embodiment of the present disclosure provides a method for monitoring an electrolyte abnormality. The electrolyte abnormality monitoring method comprises the following steps:

in some embodiments, the electrolyte anomaly monitoring method comprises: electrolyte state information is acquired, wherein,

the electrolyte state information comprises at least one of electrolyte temperature information, electrolyte flow information, current density information, electrolyte components, electrolyte impurities and cathode plate surface conditions; judging whether the state of the electrolyte is abnormal or not based on the electrolyte state information; in response to the electrolyte condition anomaly, determining a treatment protocol.

One embodiment of the present disclosure provides an electrolyte abnormality monitoring system.

In some embodiments, the electrolyte abnormality monitoring system includes an information acquisition module, a state judgment module, and an abnormality processing module; the information acquisition module is used for acquiring electrolyte state information, wherein the electrolyte state information comprises at least one of electrolyte temperature information, electrolyte flow information, current density information, electrolyte components, electrolyte impurities and cathode plate surface conditions; the state judgment module is used for judging whether the state of the electrolyte is abnormal or not based on the electrolyte state information; the abnormality processing module is used for responding to the electrolyte state abnormality and determining a processing scheme.

One of the embodiments of the present specification provides an electrolyte abnormality monitoring apparatus, including a processor, where the processor is configured to execute an electrolyte abnormality monitoring method.

One of the embodiments of the present disclosure provides a computer-readable storage medium, where the storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the electrolyte abnormality monitoring method.

Drawings

The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a schematic diagram of an application scenario of an electrolyte anomaly monitoring system according to some embodiments of the present disclosure;

FIG. 2 is an exemplary block diagram of an electrolyte anomaly monitoring system according to some embodiments described herein;

FIG. 3 is an exemplary flow diagram of an electrolyte anomaly monitoring method according to some embodiments described herein;

FIG. 4 is a schematic illustration of data acquisition of an electrolyte anomaly monitoring system according to some embodiments described herein;

FIG. 5 is an exemplary flow chart for determining whether an electrolyte condition is abnormal based on a preset normal range, according to some embodiments of the present disclosure;

FIG. 6 is an exemplary diagram of a determination processing scheme according to some embodiments of the present description;

fig. 7 is a schematic diagram of a structure of a second machine learning model 700, shown in accordance with some embodiments of the present description.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Fig. 1 is a schematic diagram of an application scenario 100 of an electrolyte anomaly monitoring system according to some embodiments of the present disclosure.

In some embodiments, the electrolyte anomaly monitoring system can determine whether the electrolyte condition is anomalous by implementing the methods and/or processes disclosed herein.

As shown in fig. 1, an application scenario 100 according to an embodiment of the present disclosure may include a processing device 110, a network 120, a terminal 130, a storage device 140, and a detection apparatus 150.

The processing device 110 may be used to process data and/or information from at least one component of the application scenario 100 or an external data source (e.g., a cloud data center). Processing device 110 may access data and/or information from terminal 130, storage device 140, and/or detection apparatus 150 via network 120. Processing device 110 may directly connect with terminal 130 and/or storage device 140 to access information and/or data. For example, processing device 110 may obtain electrolyte status information from terminal 130 and/or storage device 140. Processing device 110 may process the acquired data and/or information. For example, the processing device 110 may determine whether the electrolyte status is abnormal based on the electrolyte status information. In some embodiments, the processing device 110 may be a single server or a group of servers. The processing device 110 may be local, remote. The processing device 110 may be implemented on a cloud platform.

The network 120 may include any suitable network that provides information and/or data exchange capable of facilitating the application scenario 100. In some embodiments, information and/or data may be exchanged between one or more components of the application scenario 100 (e.g., the processing device 110, the terminal 130, the storage device 140, and the detection apparatus 150) via the network 120. Network 120 may include a Local Area Network (LAN), a Wide Area Network (WAN), a wired network, a wireless network, and the like, or any combination thereof.

Terminal 130 refers to one or more terminal devices or software used by a user. In some embodiments, the terminal 130 may be a mobile device, a tablet computer, or the like, or any combination thereof. In some embodiments, the terminal 130 may interact with other components in the application scenario 100 through the network 120. For example, the terminal 130 may send one or more control instructions to the processing device 110 to control the processing device 110 to determine whether the electrolyte status is abnormal based on the electrolyte status information. Also for example, the terminal 130 may acquire a result of whether the state of the electrolyte is abnormal from the processing apparatus 110. In some embodiments, the terminal 130 may be part of the processing device 110. In some embodiments, the terminal 130 may be integrated with the processing device 110 as an input for electrolyte status information.

Storage device 140 may be used to store data, instructions, and/or any other information. In some embodiments, storage device 140 may store data and/or information obtained from, for example, processing device 110, terminal 130, and/or the like. For example, the storage device 140 may store pre-electrolyte state information. As another example, the storage device 140 may store a trained machine learning model. In some embodiments, storage device 140 may include mass storage, removable storage, and the like, or any combination thereof.

The detection device 150 may be used to obtain electrolyte status information. In some embodiments, the detection device 150 may send the electrolyte status information to the server 110 via the network 120 for further processing by the server 110 based on the electrolyte status information. In some embodiments, the detection device 150 may include an infrared detection device 150-1, an image acquisition device 150-2, a temperature acquisition device 150-3, an ultrasound device 150-4, and a sample detection module 150-5. For more description of the detecting device 150, reference may be made to fig. 4 and its related description, which are not repeated herein.

It should be noted that the application scenario 100 is provided for illustrative purposes only and is not intended to limit the scope of the present description. It will be apparent to those skilled in the art that various modifications and variations can be made in light of the description herein. For example, the application scenario 100 may also include a database. However, such changes and modifications do not depart from the scope of the present specification.

FIG. 2 is an exemplary block diagram of an electrolyte anomaly monitoring system 200 according to some embodiments described herein. As shown in fig. 2, the electrolyte abnormality monitoring system 200 may include an information obtaining module 210 and a state determining module 220. In some embodiments, the electrolyte anomaly monitoring system 200 may further include an anomaly handling module 230. In some embodiments, one or more modules of the electrolyte anomaly monitoring system 200 may be executed by the server 110.

The information acquisition module 210 may be used to acquire electrolyte status information. In some embodiments, the electrolyte state information may include at least one of electrolyte temperature information, electrolyte flow rate information, current density information, electrolyte composition, electrolyte impurities, and cathode plate surface condition. For more description of the electrolyte state information, reference may be made to fig. 3 and fig. 4 and the related description thereof, which are not repeated herein.

The state determination module 220 may be configured to determine whether the electrolyte state is abnormal based on the electrolyte state information. In some embodiments, the state determination module 220 may determine whether the electrolyte state is abnormal based on a preset normal range. For more description of the preset normal range and the judgment of whether the state of the electrolyte is abnormal, refer to fig. 3 and fig. 5 and the related description thereof, which are not repeated herein.

The exception handling module 230 may be configured to determine a handling scheme in response to the electrolyte condition exception. In some embodiments, the treatment protocol may include at least one of an electrolyte circulation volume adjustment value and an electrolyte clear volume adjustment value. In some embodiments, the exception handling module 230 may determine at least one of the electrolyte circulation volume adjustment value and the electrolyte net volume adjustment value based on at least a portion of the electrolyte state information via a second machine learning model. For more description of the processing scheme, the adjustment value of the circulation amount of the electrolyte, the adjustment of the net water amount of the electrolyte and the machine learning model, reference may be made to fig. 3 and fig. 6 and their related description, which are not repeated herein.

It should be noted that the above description of the electrolyte anomaly monitoring system 200 and its modules is for convenience of description only and should not limit the present disclosure to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. In some embodiments, the information obtaining module 210, the status determining module 220, and the exception handling module 230 disclosed in fig. 1 may be different modules in a system, or may be a module that implements the functions of two or more modules described above. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.

FIG. 3 is an exemplary flow diagram of an electrolyte anomaly monitoring method 300, shown in accordance with some embodiments herein. As shown in fig. 3, the process 300 includes the following steps. In some embodiments, the process 300 may be performed by the electrolyte anomaly monitoring system 200.

Electrolysis is the process of producing the desired product by oxidation at the interface between the anode and the solution and reduction at the interface between the cathode and the solution when current is passed through.

The electrolytic cell refers to a cell body in which an electrolytic reaction occurs. The electrolytic cell may comprise anode plates, e.g. thick plates of raw copper (containing 99%) made beforehand, and cathode plates, e.g. stainless steel plates, thin sheets of pure copper.

At step 310, electrolyte state information is obtained. In some embodiments, this step 310 may be performed by the information acquisition module 210.

The electrolyte state information refers to information about the electrolyte. In some embodiments, the electrolyte state information includes at least one of electrolyte temperature information, electrolyte flow information, current density information, electrolyte composition, electrolyte impurities, and cathode plate surface condition. In some embodiments, the electrolyte status information may also include electrolyte color, electrolyte conductivity, and the like.

The electrolyte temperature information refers to information related to the electrolyte temperature. In some embodiments, the electrolyte temperature information includes an inter-layer temperature difference, an end-to-end temperature difference, and a bulk temperature.

In some embodiments, the temperature difference between the layers is the temperature difference between the layers of electrolyte at different levels in the cell. For example, the electrolyte temperature difference information may include a temperature difference between any two of the electrolyte surface layer, the middle layer, and the bottom layer.

In some embodiments, the temperature difference between the ends is a temperature difference at different locations in the cell, e.g., a temperature difference between the ends of the cell, a temperature difference between the middle of the cell and the ends of the cell, etc.

In some embodiments, the bulk temperature may be indicative of an average temperature of the electrolyte. In some embodiments, the bulk temperature may be an average temperature based on temperatures at different locations of the electrolyte.

The electrolyte flow rate refers to the flow circulation rate of the electrolyte in the electrolytic cell, and the electrolyte flow rate can be kept in a reasonable range to balance the components and the temperature of the electrolyte at each position in the electrolytic cell. In some embodiments, the electrolyte flow rate may affect the temperature difference between the electrolyte surface layer and the electrolyte bottom layer, e.g., too little electrolyte flow rate may result in too much temperature difference between the electrolyte surface layer and the electrolyte bottom layer.

The current density is the density at which electric charges flow, i.e., the amount of current per unit cross-sectional area. In some embodiments, the current density is correlated to electrolyte flow and electrolyte temperature differential information. For example, as the current density increases, the flow rate of the electrolyte increases, and the flow cycle of the electrolyte increases, and the temperature of the electrolyte in the electrolytic cell decreases accordingly.

The electrolyte component refers to a chemical composition component of the electrolyte, and for example, when a mixed solution of sulfuric acid and copper sulfate is used as the electrolyte, the electrolyte component may include a sulfate ion content, a copper ion content, and the like.

The electrolyte impurities refer to impurity components contained in the electrolyte and not beneficial to electrolytic production.

Taking electrolytic copper as an example, the electrolyte impurities may include antimony, bismuth, arsenic, etc., for example, as electrolytic refining proceeds, impurity elements in the anode are continuously enriched in the electrolyte, and when the impurities are enriched to a certain concentration, the impurities may deposit on the cathode together with copper, such as antimony, bismuth, etc., which seriously affects the quality of the electrolytic copper; sometimes the electrolyte is contaminated with, for example, arsenic, etc., reducing the amount of sulfuric acid and increasing the resistance of the solution, resulting in unnecessary loss of electrical energy.

The plate means a plate-like substance as an anode and/or a cathode. The plates include anode plates, e.g., thick plates made beforehand of raw copper (containing 99% copper), and cathode plates, e.g., stainless steel plates, thin sheets made of pure copper.

The cathode plate surface condition is information about the quality of the electrolytically produced metal adhering to the cathode plate. Such as the flatness, integrity, degree of texture and thickness uniformity, color, etc., of the electrolytically produced metal attached to the cathode plate.

In some embodiments, the flatness of the metal is related to whether bulges and pin-like protrusions are formed on the surface of the electrolytically generated metal, and the more bulges and pin-like protrusions are formed on the surface of the metal, the lower the flatness is; the integrity is related to whether the electrolytically produced metal completely covers the cathode plate, and the larger the area of the cathode plate not covered with metal is, the lower the integrity is; the texture refers to the number and depth of electrolytically produced metal textures attached to the cathode plate; the thickness uniformity degree is used for representing whether the thicknesses of the electrolytically produced metals attached to different positions of the cathode plate are consistent or not, and the larger the difference value between the thicknesses of the electrolytically produced metals attached to different positions of the cathode plate is, the lower the thickness uniformity degree is; color refers to the color of the electrolytically produced metal attached to the cathode plate.

In some embodiments, the higher the impurity content in the electrolyte, the poorer the degree of metal flatness, integrity, texture, and thickness uniformity of the electrolytically-produced metal surface attached to the cathode plate.

In some embodiments, the electrolyte status information may be acquired by the detection device 150. For more information on obtaining electrolyte status information, see fig. 4.

And step 320, judging whether the electrolyte state is abnormal or not based on the electrolyte state information. Step 320 may be performed by the state determination module 220.

The electrolyte state abnormity means that the state information of the electrolyte is beyond the normal range of the electrolyte state. In some embodiments, an abnormal electrolyte condition may result in abnormal electrolysis production, e.g., decreased electrolysis quality, increased electrolysis energy consumption, etc.

In some embodiments, the electrolyte state abnormality may include an electrolyte temperature information abnormality, an electrolyte flow information abnormality, a current density information abnormality, an electrolyte composition abnormality, an electrolyte impurity abnormality, a cathode plate surface condition abnormality, and the like.

In some embodiments, the abnormality of the electrolyte temperature information indicates that the temperature of the electrolyte exceeds a normal range of the electrolyte temperature, for example, the electrolyte temperature is too low or the electrolyte temperature is too high. The low temperature of the electrolyte can cause crystallization near the cathode plate, and the high temperature of the electrolyte can cause uneven crystallization on the surface of the cathode plate, so that the quality of metal produced by subsequent electrolysis can be affected in both cases. In some embodiments, the electrolyte temperature information anomaly may be an inter-layer temperature anomaly of the electrolyte, an inter-terminal temperature anomaly of the electrolyte, and/or an overall temperature anomaly of the electrolyte.

In some embodiments, the abnormal electrolyte flow information indicates that the flow circulation amount of the electrolyte in the electrolytic cell exceeds the normal flow circulation amount range, and may be, for example, too small electrolyte flow, too large electrolyte flow, or the like. Because the ion diffusion speed of the cathode plate is low due to the fact that the flow of the electrolyte is too small, a small amount of impurity ions or hydrogen ions and target metal ions are separated out on the cathode together, the surface of the target metal separated out on the cathode plate is not flat due to the fact that the flow of the electrolyte is too large, and the quality and the efficiency of the target metal generation are affected under the two conditions.

In some embodiments, the current density information anomaly is that the current density on the cathode plate or the anode plate exceeds a normal range of current density, for example, the current density may be too low or too high. Too low current density can result in too low electrolyte flow, which results in slower ion diffusion rate of the cathode plate, and too high current density can result in too high electrolyte flow, which affects the quality and efficiency of the generated target metal.

In some embodiments, the abnormal electrolyte composition means that the concentration of the target component in the electrolyte exceeds a normal concentration range, for example, the concentration of an ion may be too high or too low. The concentration of the target component (e.g., copper ions) can directly impact the viscosity and conductivity of the electrolyte, thereby affecting the quality and efficiency of the target metal being produced.

In some embodiments, the electrolyte impurity abnormality refers to the electrolyte having an impurity content exceeding a normal impurity content range, for example, antimony, bismuth, etc. exceeding the normal content range. When the content of impurities in the electrolyte is too high, floating anode mud is easy to form and adhere to a cathode plate, and the quality and the efficiency of generating target metal are influenced.

In some embodiments, the cathode plate surface condition abnormality refers to an abnormality in the quality of electrolytically produced metal attached to the cathode plate. For example, at least one of the flatness, integrity, degree of grain and thickness uniformity, color, etc. of the electrolytically produced metal surface attached to the cathode plate is abnormal. For example, the flatness of the metal surface is less than at least one of a preset flatness threshold, the integrity is less than a preset integrity threshold, the number of lines exceeds a preset number threshold, the depth of lines exceeds a preset depth threshold, the uniformity of lines and thickness is lower than a preset uniformity of thickness, the color is not a preset color, and the like.

In some embodiments, the state determination module 220 may determine whether the electrolyte state is abnormal based on the electrolyte state information in various ways.

In some embodiments, the state determination module 220 may determine whether the electrolyte state is abnormal based on a comparison of the electrolyte state information and expert database information, for example, comparing the electrolyte state information with historical data or standard specifications in the expert database. The expert database may be built based on historical data and/or information that invokes existing standard specifications.

In some embodiments, the state determination module 220 may also determine whether the electrolyte state information is abnormal through the first machine learning model. For example, based on the processing of the electrolyte state information collected by the information acquisition module 210 by the first machine learning model, it is determined whether the electrolyte state information is abnormal.

In some embodiments, the input to the first machine learning model may be electrolyte state information, e.g., a combination of one or more of electrolyte temperature information, electrolyte flow information, current density information, electrolyte composition, electrolyte contamination, and cathode plate surface condition, and the output of the first machine learning model is a determination of whether the electrolyte state information is abnormal, e.g., normal or abnormal. In some embodiments, the first machine learning model may be a K-nearest neighbor algorithm (KNN) model.

In some embodiments, the first machine learning model may be trained based on a number of training samples with identifications. Specifically, a training sample with an identifier is input into the first machine learning model, and parameters of the first machine learning model are updated through training. In some embodiments, the training samples may include electrolyte temperature information, electrolyte flow information, current density information, electrolyte composition information, electrolyte contamination information, and cathode plate surface condition information.

In some embodiments, the identification may be that the electrolyte state information corresponding to the training sample is normal or abnormal. In some embodiments, the identifier may be obtained by comparing and judging with a preset normal range threshold or expert database information. In some embodiments, training may be performed by various methods based on the training samples. For example, the training may be based on a gradient descent method.

In some embodiments, whether the state of the electrolyte is abnormal may be judged by a preset normal range. The preset normal range is a range for representing each index related to the electrolyte during normal electrolysis, for example, the electrolyte abnormality monitoring system may obtain the preset normal range, and if the electrolyte state information does not conform to the preset normal range, the electrolyte state is abnormal. For more description of the preset normal range, refer to fig. 6 and its related description, which are not repeated herein.

In some embodiments, the electrolyte anomaly monitoring system may determine an electrolyte anomaly handling scheme.

The electrolyte abnormality treatment scheme refers to a measure for restoring the state of the electrolyte to a normal range. In some embodiments, the electrolyte exception handling scheme comprises: changing the circulating amount of the electrolyte, replacing the electrolyte in the electrolytic cell, increasing the purifying amount in the circulating process of the electrolyte and the like. For example, measures such as increasing the heat exchange amount by steam, changing the flow rate of a flow pump, increasing the ionic component or the diluent, and the like are taken.

In some embodiments, for different types of electrolyte state anomalies, the state determination module 220 may determine a corresponding processing scheme.

In some embodiments, when the electrolyte temperature information is abnormal, the corresponding processing scheme determined by the state determination module 220 may be at least one of adjusting a flow rate of the electrolyte, adjusting a current density, replacing a part of the electrolyte, and the like, so as to adjust the temperature of the electrolyte.

In some embodiments, the status determination module 220 may determine the corresponding processing scheme according to the degree of abnormality of the electrolyte temperature information. For example, when the electrolyte temperature information is mild, the corresponding processing scheme determined by the state determination module 220 may be to adjust the flow rate of the electrolyte and/or adjust the current density. For another example, when the electrolyte temperature information is abnormal to be medium, the corresponding processing scheme determined by the state determination module 220 may be to replace a part of the electrolyte. For another example, when the electrolyte temperature information is abnormally heavy, the corresponding processing scheme determined by the state determination module 220 may be to replace all the electrolyte.

In some embodiments, the state determination module 220 may determine the degree of abnormality of the electrolyte temperature information by establishing an expert database based on industry experience, historical data, and the like.

In some embodiments, when the electrolyte flow information is abnormal, the corresponding processing scheme determined by the state determination module 220 may be to adjust at least one of current density and the like, for example, when the electrolyte flow is too small, the current density is increased to increase the electrolyte flow.

In some embodiments, when the current density information is abnormal, the corresponding processing scheme determined by the state determination module 220 may be at least one of adjusting the cell voltage, adjusting the sulfuric acid concentration in the electrolyte, adjusting the metal ion concentration, and the like. Illustratively, when the current density is too low, the voltage of the electrolyzer is increased to increase the current density. Further illustratively, when the current density is too high, the concentration of sulfuric acid in the electrolyte is decreased or the concentration of metal ions in the electrolyte is increased to decrease the current density.

In some embodiments, when the electrolyte composition is abnormal, the corresponding processing scheme determined by the state determination module 220 may be to add a corresponding solution. For example, when the concentration of copper ions in the electrolyte is too high, a certain amount of water may be added to the electrolyte to reduce the copper ion concentration.

In some embodiments, when electrolyte impurities are abnormal, the corresponding processing scheme determined by the state determination module 220 may be to purge at least a portion of the electrolyte. In some embodiments, the status determination module 220 may determine the proportion of the electrolyte that needs to be purified according to the degree of abnormality of the electrolyte impurities. For example, the more serious the degree of abnormality of the electrolyte impurities, the higher the corresponding purge ratio. In some embodiments, the state determination module 220 may determine the degree of electrolyte impurity abnormality by establishing an expert database based on industry experience, historical data, and the like.

In some embodiments, the treatment protocol determined by the status determination module 220 may be adjusting the additive amount of the additive when the surface condition of the cathode plate is abnormal. For example, when the surface of the electrolytically-generated metal is textured, the amount of bone cement to be added needs to be increased.

In some embodiments, by evaluating the degree of abnormality of the electrolyte state, a processing scheme more suitable for the current electrolyte state can be quickly adopted to realize quick adjustment of the electrolyte to a normal state.

In some embodiments, determining the electrolyte exception handling scheme includes determining at least one of an electrolyte circulation volume adjustment value and an electrolyte clear volume adjustment value.

The electrolyte circulation amount adjusting value refers to a change value of the electrolyte circulation amount which is determined by the system and is required to be changed for adjusting the state of the electrolyte to a normal state. For example, when the electrolyte abnormality monitoring system detects that the electrolyte temperature difference exceeds a threshold value, the abnormal state processing module can increase the electrolyte circulation amount adjusting value, so that the circulation of the electrolyte in the electrolytic cell is enhanced, and the temperature difference of the electrolyte is reduced.

The electrolyte water purification amount adjusting value refers to a change value of the electrolyte water purification amount which is determined by a system and is required to be changed for adjusting the electrolyte state to a normal state. For example, when the electrolyte abnormality monitoring system detects that the impurity content in the electrolyte is too high, the abnormal state processing module can increase the change value of the net water amount of the electrolyte, so as to reduce the concentration of the electrolyte impurities.

In some embodiments, determining an electrolyte exception handling scheme includes determining to adjust an electrolyte related parameter. In some embodiments, adjusting the electrolyte-related parameter includes an electrolyte circulation adjustment value and an electrolyte clear water adjustment value. In some embodiments, the exception handling module 230 may determine the electrolyte circulation adjustment value and the electrolyte net water adjustment value from the production history adjustment records.

In some embodiments, determining the electrolyte abnormality treatment scheme includes determining corresponding electrolyte circulation volume adjustment values and water purification volume adjustment values based on the electrolyte layer/end temperature difference, the cathode plate surface real-time state value, and the impurity content ratio value.

In some embodiments, determining the circulation volume adjustment value and the net water volume adjustment value may be accomplished in a variety of ways, such as manually based on empirical acquisition, automatically based on a predetermined prediction table, based on a multiple linear regression fit, and the like.

In some embodiments, the exception handling module 230 may determine the electrolyte circulation adjustment value and/or the electrolyte net water adjustment value by a multiple linear regression fit. For example, the relationship between at least part of the electrolyte state information and the electrolyte circulation amount adjustment value and/or the electrolyte purified water amount adjustment value is fitted based on a least square method, and then the electrolyte circulation amount adjustment value and/or the electrolyte purified water amount adjustment value is calculated according to at least part of the electrolyte state information. For more description of the multiple linear regression fitting, refer to fig. 6 and its related description, which are not repeated herein.

In some embodiments, the exception handling module 230 may determine the electrolyte circulation volume adjustment value and/or the electrolyte net water volume adjustment value via a second machine learning model. For example, an LSTM machine learning model is adopted, the model input is the temperature difference between layers/ends, the surface real-time state value of the cathode plate and the impurity content proportion value, and the output of the model is the circulation quantity regulating value and the purified water quantity regulating value. For more description of the second machine learning model, refer to fig. 6 and its related description, which are not repeated herein.

In some embodiments, the electrolyte state information is obtained to judge whether the electrolyte state is abnormal, so that the electrolytic production can be timely adjusted when the electrolysis is abnormal, and the efficiency and the quality of the electrolytic production are improved.

It should be noted that the above description of the process 300 is for illustration and description only and is not intended to limit the scope of the present disclosure. Various modifications and changes to flow 300 will be apparent to those skilled in the art in light of this description. However, such modifications and variations are still within the scope of the present specification.

FIG. 4 is a schematic illustration of data acquisition by an electrolyte anomaly monitoring system according to some embodiments described herein.

The electrolyte state information 420 is acquired by the detection device 150.

The electrolyte anomaly monitoring system can obtain electrolyte status information 420 in the electrolysis system 410 through the detection device 150.

In some embodiments, electrolysis system 410 may include a plurality of electrolysis cells, such as electrolysis cell 410-1, electrolysis cells 410-2, …, and electrolysis cell 410-n.

In some embodiments, the electrolyte state information 420 may include electrolyte temperature information, electrolyte flow information, current density information, electrolyte composition, electrolyte impurities, and cathode plate surface condition information.

The detection device 150 is a device for detecting relevant data (e.g., detection information) of the electrolyte.

In some embodiments, the detection device 150 may obtain the detection information based on at least one of image, infrared, laser, sampling, and ultrasound. In some embodiments, the detection device 150 may include an infrared detection device 150-1, an image acquisition device 150-2, a temperature acquisition device 150-3, an ultrasound device 150-4, and a sample detection module 150-5, among other devices. The detection device 150 with the corresponding data acquisition function may be selected based on different electrolyte state information 420 to be acquired, and in some embodiments, one or more types of electrolyte state information 420 may be acquired based on one type of detection device 150.

In some embodiments, the infrared detection device 150-1 may be used to obtain temperature information of the sample to be tested. For example, the infrared detection device 150-1 (e.g., thermal infrared imager, etc.) may perform infrared thermal imaging on the sample to be tested, receive infrared specific band signals thermally radiated by the sample to be tested, convert the signals into images and graphs capable of being distinguished by human vision, and further calculate the temperature value of the sample to be tested. In some embodiments, the infrared detection device 150-1 may also analyze the cathode plate surface quality through images of infrared thermal imaging.

In some embodiments, the infrared detection device 150-1 may also be used to obtain location information of the sample to be tested. For example, the infrared detection device 150-1 may collect an infrared thermal image of the electrolytic cell by a thermal imager, process the infrared thermal image to obtain a pixel point of the polar plate with abnormal temperature, and finally obtain a corresponding position of the polar plate according to the pixel point.

In some embodiments, an image capture device 150-2 (e.g., a camera, an image sensor, etc.) may be used to capture image information of the electrolyte and/or the plate. For example, the image capturing device 150-2 may capture image information and perform image recognition to obtain information such as color, texture, shape, and spatial relationship of the cathode plate and/or the anode plate during or after the electrolysis production. Image recognition refers to the processing, analysis, and understanding of images with a computer to recognize various patterns of objects and objects. In some embodiments, the detection device 150 may perform steps including image acquisition, image pre-processing, feature extraction, image recognition, and the like.

In some embodiments, the image capture device 150-2 may capture images using infrared thermal imaging. In some embodiments, the image capturing device 150-2 may convert the captured infrared image into an image and a graph for human visual recognition, and may further calculate a temperature value.

In some embodiments, an image capture device 150-2 (e.g., a camera) may be positioned directly above the electrolytic cell to capture images of the cathode plate surface topography information in real time. In some embodiments, the detection device 150 may perform image recognition through a third machine learning model based on the acquired image to determine a uniformity value of the surface of the cathode plate.

In some embodiments, the third machine learning model for image recognition may be a CNN model. In some embodiments, the input of the third machine learning model for image recognition is the cathode plate surface image obtained by the camera device, and the output of the third machine learning model for image recognition is the specific uniformity value of the cathode plate surface.

In some embodiments, the third machine learning model may be trained based on a number of training samples with identifications. For example, the training samples with the identifications are input into a third machine learning model, and the parameters of the third machine learning model are updated through training. In some embodiments, the training sample may be one or more combinations of data collected by laser, infrared, ultrasonic, terahertz, etc. devices. In some embodiments, the indicia may be a specific uniformity value of the surface of the cathode plate. In some embodiments, the identifier may be obtained by measuring the surface thickness of the cathode plate to obtain actual thickness data of the cathode plate. In some embodiments, training may be performed by various methods based on the training samples. For example, the training may be based on a gradient descent method.

In some embodiments, the image recognition through the third machine learning model can reduce the detection cost and improve the detection practicability. In some embodiments, the limitation of detection in liquid acid and alkali environments by means of laser and terahertz can be solved by performing image recognition through a third machine learning model.

In some embodiments, a temperature detection device 150-3 (e.g., a temperature sensor, etc.) may be used to collect temperature information of the electrolyte. The temperature information of the electrolyte may comprise at least two electrolyte temperatures measured at least two different depths of the electrolytic cell, wherein the difference between the electrolyte temperatures measured at the two depths is the temperature difference between the electrolyte layers. The temperatures of the intermediate and bottom electrolyte layers may be measured by one or more temperature acquisition devices 150-3 (e.g., contact resistance thermometers) disposed at corresponding locations.

In some embodiments, the temperature information of the electrolyte may further include the temperature of the electrolyte at the water inlet end and the water outlet end of the electrolyte, wherein the difference between the temperatures of the electrolyte measured at the water inlet end and the water outlet end is the temperature difference between the electrolyte ends. In some embodiments, the temperature detection module 150-3 may include two temperature sensors mounted at the inlet and outlet ends of the electrolyte, respectively.

In some embodiments, the temperature sensing device 150-3 may include a plurality of temperature sensors installed at different locations of the electrolytic cell, such as a temperature sensor installed in the electrolyte for obtaining the temperature of the electrolyte, and a temperature sensor installed on the surface of the plate for obtaining the temperature of the plate.

In some embodiments, the ultrasonic detection module 150-4 (e.g., a pulse-echo ultrasonic detector) may collect quality information of the surface of the cathode plate (e.g., surface flatness, integrity, texture, and uniformity of thickness of the plate, etc.).

In some embodiments, the sampling test device 150-5 may randomly sample the electrolyte or the plate to analyze the chemical composition of the sample to be tested. For example, the sampling detection device 150-5 may adopt an ion chromatograph to randomly sample the electrolyte and analyze the composition information of the electrolyte, and for example, the sampling detection device 150-5 may adopt a metal composition analyzer to randomly sample the cathode plate sample, sample the cathode plate by punching holes, and analyze the chemical composition of the sample; for another example, the sampling device 150-5 can sample the bump on the surface of the cathode plate to perform chemical composition analysis, illustratively, to analyze the purity of the metal product (e.g., copper) and the impurity condition, wherein the impurity condition includes the type and content of the main impurities (e.g., silver and its content).

In some embodiments, the detection device 150 may also include other components for obtaining the electrolyte status information 420, for example, the detection device 150 may also include an electromagnetic flow meter for obtaining electrolyte flow information.

In some embodiments, the detection device 150 may further include a positioning assembly, which may be used to position the faulty plate. In some embodiments, the positioning component may process the infrared thermal image collected by the infrared detection device 150-1, determine a pixel point with abnormal temperature, and finally determine a corresponding position of the fault plate according to the pixel point with abnormal temperature.

In some embodiments, the detection device 150 may further include a laser thickness gauge for measuring the plate thickness. For example, the thickness of the cathode plate at different locations may be measured by a laser thickness gauge.

In some embodiments, the detection device 150 may further comprise a smart socket, a smart meter, or the like, for collecting the cell voltage, current density information, or the like, in the electrolytic cell. In some embodiments, the detection device 150 may further include a flow collector (e.g., a flow meter) for obtaining electrolyte flow information.

The various detection modes shown in some embodiments of the present disclosure may be performed sequentially. The method can obtain more accurate results in various ways for the same sample to be detected (such as electrolyte, cathode plate and/or anode plate in production or after being taken out of a tank), and can also perform primary detection in a part of ways, and determine the target and the way of further detection according to the detection result. For example, the detection device 150 may perform a preliminary detection on the electrolyte composition, and if it is detected that the impurity content of the electrolyte is relatively high, the detection device 150 may further perform a sampling detection on the electrode plate to determine whether the quality of the electrode plate is abnormal.

In some embodiments, the detection may be accomplished by moving the sample to be tested by a mobile device. A moving device refers to a device that can be used to move a sample to be tested to a detection area. In some embodiments, the moving device may be a conveyor, a translation device, or the like.

In some embodiments, the sample to be tested may be detected by the detection device 150 after being moved to the detection area by the mobile device. For example, the grooved electrode plate is placed on the traversing device through a mechanical arm (or a pulley), and corresponding detection is carried out when the electrode plate moves to the position of the detection device. In some embodiments, the robotic arm (or trolley) may place samples to be tested or tested in corresponding locations, e.g., to move respectively acceptable and unacceptable plates to different transport lanes, and, for example, to place plates requiring chemical composition analysis in corresponding test zones.

In some embodiments, the detection device 150 may be moved by a mobile device to accomplish the detection. For example, the detecting device 150 may be mounted on a moving device, and the moving device is configured to move the detecting device 150 to obtain the electrolyte state information 420 of the electrolytic cell at different positions.

In some embodiments, the mobile device may be installed on the ground or the ceiling, and when there is a detection demand, the detection device 150 moves the detection device to the corresponding detection area through the transmitted instruction; in some embodiments, the mobile device may adaptively adjust the distance, angle, etc. between the detection device and the detection target to obtain the best detection effect.

In some embodiments, the mobile device may be a mobile robot. For example, one or more mobile robots, each equipped with a detection device 150, may be installed in the electrolysis production plant as required. In some embodiments, corresponding instructions may be sent to the robot based on different detection requirements to control the mobile robot to move to the corresponding detection area for detection. In some embodiments, the mobile robot may also be controlled to move to the corresponding detection area for detection according to a preset rule.

In some embodiments, the mobile robot may drive the detection device 150 to move to randomly draw the cathode plate for punching and sampling. In some embodiments, each cathode plate has one or more positioning marks, and the mobile robot receives the instruction and then moves the sampling detection device 150-5 to the extracted position of the cathode plate according to the positioning marks on the cathode plate for sampling. In some embodiments, the plate position may be adjusted by the cooperation of the sensing device 150 and the robotic arm to facilitate sampling by the sample sensing device 150-5.

In some embodiments, the mobile robot may move the detection device 150 to perform random sampling for chemical analysis. For example, the mobile robot may move the sampling inspection device 150-5 to a focused monitoring area to perform chemical analysis on the plate or electrolyte.

In some embodiments, the mobile device can improve the flexibility of detection by the detection device and reduce the burden of manual movement of the detection device.

In some embodiments, the mobile device may be a drone. Electrolyte abnormity monitored control system can send target position information to the unmanned aerial vehicle that is furnished with detection device, and the detection device's that receives unmanned aerial vehicle and return positional information.

In some embodiments, the electrolyte abnormality monitoring system receives the electrolyte state information of the target position when the detection device is at the target position.

In some embodiments, the drone may utilize optical flow techniques to achieve indoor positioning. For example, the unmanned aerial vehicle may convert information such as pixel distribution, color, and brightness into digital signals by using an optical flow sensor built therein, transmit the digital signals to an image processing system or a processing system of an image recognition module, perform various calculations to extract features of a target, and control the operation of the unmanned aerial vehicle according to the result of the determination.

In some embodiments, the drone may be controlled to be high indoors by an ultrasonic sensor. For example, the drone may discriminate relative altitude by an ultrasonic sensor. In some embodiments, the drone may also detect changes in the attitude of the aircraft through an IMU (inertial measurement) and make adjustments in real time. In some embodiments, the drone may be computed by an efficient vision processor to enable precise indoor positioning hovering and smooth flight for the drone.

In some embodiments, the detection device 150 can adjust the position of the plate by a robotic arm, sled, or the like. For example, the mechanical arm, the pulley, the polar plate moving, the polar plate position adjusting and the like can be adopted for detection, and meanwhile, the polar plate is sorted by receiving an instruction based on the detected polar plate abnormal condition.

In some embodiments, the detection device 150 may further include an auxiliary light, and the detection device 150 may adjust the auxiliary light. For example, the detection device 150 may adjust the detected auxiliary lights according to lighting conditions at different time periods, for example, in the morning or evening, the light may be insufficient in the electrolytic production plant, and the detection device 150 may automatically adjust the lights according to the current brightness level of the electrolytic production plant and the brightness level required for detection, so as to perform intelligent light supplement. For another example, the detection device 150 may adjust the auxiliary light according to the detection requirement, for example, the detection device 150 may control the brightness level of the auxiliary light according to the different brightness degrees of the light required by different detection modes.

In some embodiments, unmanned aerial vehicle can improve the flexibility that detection device detected greatly, promotes the operation scope that detection device covered. In some embodiments, the auxiliary light is adjusted to improve the quality of the image collected by the detection device and improve the detection accuracy.

Fig. 5 is an exemplary flowchart illustrating the determination of whether the state of the electrolyte is abnormal based on a preset normal range according to some embodiments of the present disclosure. As shown in fig. 5, the process 500 includes the following steps. In some embodiments, the process 500 may be performed by the state determination module 220.

Step 510, obtaining a preset normal range.

The preset normal range is used for representing the range of each index related to the electrolyte during normal electrolysis. In some embodiments, the preset normal range may include a range of indicators associated with the electrolyte, such as electrolyte flow rate, and the like, in some embodiments. In some embodiments, the predetermined normal range may further include a range of indicators associated with the plate, such as plate temperature, etc.

In some embodiments, the predetermined normal range may include an index range related to the electrolyte temperature information, such as a global temperature range, an inter-layer temperature difference range, an end-to-end temperature difference range, and the like. For more description of the electrolyte temperature information, reference may be made to fig. 3, 4 and their associated description.

The overall temperature range may be indicative of the range in which the temperature of the electrolyte is normal, for example, 55 ° to 62 °. The range of the temperature difference between the layers may represent a range in which the difference between the temperature of the surface layer of the electrolyte and the temperature of the bottom layer of the electrolyte is in a normal state of the electrolyte, for example, -2 ° to 2 °. The end-to-end temperature difference range may represent a range in which the difference in temperature of the electrolyte at both ends of the electrolytic cell at normal times of the electrolyte is within, for example, -1 ° to 1 °.

In some embodiments, the preset normal range may further include an electrolyte flow range, and the electrolyte flow range may represent a range in which the electrolyte flow is normal, for example, 6L/h to 6.2L/h. For more description of the electrolyte flow, reference may be made to fig. 3, 4 and their associated description.

In some embodiments, the predetermined normal range may also include a current density range, which may characterize the range in which the current density is normal for the electrolyte. For example, the current density can range from 2A per square meter to 4A per square meter.

In some embodiments, the predetermined normal range may further include an index range related to the composition of the electrolyte, for example, a content range of each ion, such as a copper ion content range, a sulfate ion content range, an aluminum ion content range, a trivalent chromium content range, a chloride ion content range, and the like. For more description of the electrolyte composition, electrolyte impurities, reference may be made to fig. 3, 4 and their associated description.

Each ion content range can be characterized in that the ion content of the electrolyte is in a range of 0mg/L to 30mg/L, 0mg/L to 50mg/L in sulfate ion, 0mg/L to 25mg/L in aluminum ion, 0mg/L to 10mg/L in trivalent chromium, and 0mg/L to 200mg/L in chloride ion.

In some embodiments, the preset normal range may further include an electrolyte impurity content range. For example, the impurity content of antimony ranges from 0ppm to 1 ppm. For example, the impurity content of bismuth is in the range of 0ppm to 5 ppm.

In some embodiments, the predetermined normal range may further include an index range related to the quality of the electrolytically produced metal attached to the cathode plate. For example, a metal surface flatness range, a completeness range, a grain range, a thickness uniformity range, and a target color.

The metal surface flatness range can represent the number of surface bulges and/or needle-like protrusions of the metal, for example, 0-10; the integrity range can represent the proportion of the area range of the cathode plate attached with metal produced by electrolysis in normal electrolyte to the surface area of the cathode plate, for example, 95-100%; the grain range can represent the number range (for example, 0-5 grains) and the depth range (0-1 mm) of grains when the electrolyte is normal; the thickness uniformity range can represent the difference range of the maximum thickness and the minimum thickness of the electrolytically produced metal attached to the cathode plate when the electrolyte is normal, for example, 0-5 mm; the target color may be the color of the metal produced electrolytically when the electrolyte is normal.

In some embodiments, the range of indicators relating to the quality of the electrolytically produced metal attached to the cathode plate may also include a range of purities of copper in the electrolytically produced metal on the cathode plate, and the like. The copper purity range may be indicative of the range of copper purity in the metal produced by electrolysis on the cathode plate at normal times of the electrolyte, for example, 99.95% to 99.9935%. In some embodiments, the predetermined normal range may further include other index ranges, such as a range of electrolyte moisture content, and the like, for example, 10ppm to 20 ppm.

In some embodiments, the state determination module 220 may determine the preset normal range based on a plurality of historical normal electrolyte state information, wherein the historical electrolyte state information may be electrolyte state information obtained at a certain time point in the past when electrolysis is normal.

In some embodiments, the state determination module 220 may determine the corresponding preset normal range based on a maximum value and a minimum value of each indicator in the plurality of historical normal electrolyte state information. For example, in the electrolyte state information acquired at time points 1 to 10 for the entire temperature range, the electrolyte temperatures are 55 ℃, 57 ℃, 58 ℃, 60 ℃, 56 ℃, 59 ℃, 62 ℃, 61 ℃, 55 ℃ and 56 ℃, respectively, and the maximum value and the minimum value of the corresponding entire temperature range are 62 ℃ and 55 ℃, respectively, and the entire temperature range can be determined to be 55 ° to 62 °.

In some embodiments, the state determination module 220 may obtain the preset normal range by establishing an expert database based on industry experience, historical data, and other information.

In some embodiments, the state determination module 220 may further obtain the preset normal range in other manners, for example, the state determination module 220 may further obtain the preset normal range from the terminal 130, the storage device 140, or an external data source.

And 510, judging whether the state of the electrolyte is abnormal or not based on a preset normal range. In some embodiments, step 520 may be performed by the state determination module 220.

In some embodiments, the state determination module 220 may determine whether the electrolyte state is abnormal based on whether the electrolyte state information conforms to a preset normal range. For example, the state determination module 220 may determine that the electrolyte state is abnormal when the electrolyte state information does not conform to at least one of the preset normal ranges.

In some embodiments, the state determination module 220 may determine whether the electrolyte state is abnormal based on the electrolyte temperature information. For example, when the overall temperature of the electrolyte is greater than the maximum value of the overall temperature range or less than the minimum value of the overall temperature range, the electrolyte state is abnormal.

In some embodiments, the state determination module 220 may determine whether the electrolyte state is abnormal based on the electrolyte flow information. For example, when the electrolyte flow rate is less than the minimum value of the electrolyte flow rate range, the electrolyte state is abnormal.

In some embodiments, the state determination module 220 may determine whether the electrolyte state is abnormal based on the current density information. For example, when the current density is less than the minimum value of the current density range, the state of the electrolyte is abnormal.

In some embodiments, the state determination module 220 may determine whether the electrolyte state is abnormal based on the electrolyte composition. For example, when at least one component in the electrolyte is greater than the maximum value of the corresponding content range or less than the minimum value of the corresponding content range, the state of the electrolyte is abnormal.

In some embodiments, the state determination module 220 may determine whether the electrolyte state is abnormal based on electrolyte impurities. For example, when the content of the at least one impurity in the electrolyte is greater than the maximum value of the corresponding content range or less than the minimum value of the corresponding content range, the state of the electrolyte is abnormal.

In some embodiments, the state determination module 220 may determine whether the electrolyte state is abnormal based on the cathode plate surface condition. For example, the state determining module 220 may determine whether the electrolyte state is abnormal based on at least one of the flatness, the integrity, the texture, the thickness uniformity, and the color of the metal surface, for example, when the difference of the metal thickness of the surface of the cathode plate at different positions is greater than the maximum value of the thickness uniformity range, the electrolyte state is abnormal.

In some embodiments, by setting the preset normal range, the efficiency and accuracy of determining whether the state of the electrolyte is abnormal based on the electrolyte state information can be improved.

FIG. 6 is an exemplary diagram of a determination processing scheme, shown in accordance with some embodiments of the present description.

In some embodiments, the anomaly processing module 230 may calculate the electrolyte circulation amount adjustment value 631 and/or the electrolyte net water amount adjustment value 632 from at least part 610 of the electrolyte state information via at least one historical abnormal electrolyte state information, wherein the historical abnormal electrolyte state information may be electrolyte state information acquired at a past point in time when an electrolyte state is abnormal, and the historical electrolyte circulation amount adjustment value and/or the historical electrolyte net water amount adjustment value may be determined based on at least a portion of the historical abnormal electrolyte state information.

In some embodiments, the exception handling module 230 may obtain at least a portion of the historical abnormal electrolyte status information and its corresponding historical electrolyte circulation volume adjustment value and/or historical electrolyte clean water volume adjustment value from the terminal 130, the storage device 140, or an external data source.

In some embodiments, the anomaly handling module 230 may obtain a correspondence between at least one of the electrolyte circulation volume adjustment value 631 and the electrolyte net water volume adjustment value 632 and at least a portion 610 of the electrolyte status information by a multiple linear regression fitting 621; at least one of the electrolyte circulation amount adjustment value 631 and the electrolyte net water amount adjustment value 632 is determined based on at least a part 610 of the electrolyte state information by the correspondence relationship, and at least one of the electrolyte circulation amount adjustment value 631 and the electrolyte net water amount adjustment value 632 is determined based on at least a part 610 of the electrolyte state information by the second machine learning model 622.

In some embodiments, the anomaly processing module 230 may establish a correspondence between at least a portion 610 of the electrolyte state information and the electrolyte circulation adjustment value 631 and/or the electrolyte net water amount adjustment value 632 based on a multiple linear regression equation set, wherein the independent variables of the multiple linear regression equation set may include the at least a portion 610 of the electrolyte state information and the dependent variables of the multiple linear regression equation set may include the electrolyte circulation adjustment value 631 and/or the electrolyte net water amount adjustment value 632.

For example, the independent variable of the multiple linear regression equation set may be electrolyte temperature information, electrolyte flow rate information, and current density information, and the dependent variable of the multiple linear regression equation set may be the electrolyte circulation amount adjustment value 631. As another example, the independent variables of the multiple linear regression equation set may be electrolyte temperature information, electrolyte flow rate information, current density information, electrolyte composition, electrolyte impurities, and cathode plate surface condition, and the dependent variables of the multiple linear regression equation set may be the electrolyte circulation amount adjustment value 631 and the electrolyte purified water amount adjustment value 632.

In some embodiments, the anomaly processing module 230 may substitute at least a portion of the historical abnormal electrolyte state information into an independent variable of the multiple linear regression equation set and substitute its corresponding historical electrolyte circulation amount adjustment value and/or historical electrolyte net water amount adjustment value into a dependent variable of the multiple linear regression equation set, and solve the multiple linear regression equation set based on least squares or the like to obtain parameters of the multiple linear regression equation set, thereby obtaining a correspondence between at least a portion 610 of the electrolyte state information and the electrolyte circulation amount adjustment value 631 and/or the electrolyte net water amount adjustment value 632.

In some embodiments, the correspondence between at least a portion 610 of the electrolyte state information and the electrolyte circulation amount adjustment value 631 and/or the electrolyte net water amount adjustment value 632 is fitted by multiple linear regression such that the electrolyte circulation amount adjustment value 631 and/or the electrolyte net water amount adjustment value 632 determined based on the at least a portion 610 of the electrolyte state information is more accurate.

In some embodiments, the exception handling module 230 may obtain a correspondence between the historical electrolyte circulation volume adjustment value and/or the historical electrolyte net water volume adjustment value and the handling scheme based on the second machine learning model 622. Wherein the input of the second machine learning model 622 may be at least a portion 610 of the electrolyte status information, and the output of the second machine learning model 622 may be the electrolyte circulation amount adjustment value 631 and/or the electrolyte purified water amount adjustment value 632.

In some embodiments, the anomaly handling module 230 may train the initial second machine learning model 622 via a plurality of labeled training samples, wherein one training sample corresponds to one historical abnormal electrolyte state information, the training sample may include at least a portion of the historical abnormal electrolyte state information, and the label of the training sample may include a historical electrolyte circulation regulation value and/or a historical electrolyte clean water regulation value corresponding to the historical abnormal electrolyte state information.

In some embodiments, the anomaly handling module 230 may train the initial second machine learning model 622 multiple times in a common manner (e.g., gradient descent, etc.) until the trained initial second machine learning model 622 satisfies a preset condition, and use the trained initial second machine learning model as the second machine learning model 622 for predicting the electrolyte circulation regulation value and/or the electrolyte pure water regulation value. The preset condition may be that the loss function of the updated initial basic model is smaller than a threshold, convergence, or that the number of training iterations reaches a threshold.

In some embodiments, the second machine learning model 622 may also be pre-trained by the processing device 110 or a third party and saved in the storage device 140, and the exception handling module 230 may invoke the second machine learning model 622 directly from the storage device 140.

In some embodiments, the second machine learning model 622 may be an RNN (Current Neural network) model, an LSTM (Long Short-Term Memory), or the like.

For more description of the structure of the second machine learning model 622, reference may be made to fig. 7 and its related description, which are not repeated herein.

In some embodiments, the electrolyte circulation amount adjustment value 631 and/or the electrolyte net water amount adjustment value 632 determined based on at least a portion 610 of the electrolyte state information may be made more accurate by the second machine learning model 622.

In some embodiments, the electrolyte circulation adjustment value 631 and/or the electrolyte clear water adjustment value 632 determined by the exception handling module 230 may be a fusion value obtained by fusing the adjustment values respectively determined by the multiple linear regression fitting 621 and the second machine learning model 622.

In some embodiments, the exception handling module 230 may determine the electrolyte circulation volume adjustment value (i.e., the first electrolyte circulation volume adjustment value) and/or the electrolyte net volume adjustment value (i.e., the first electrolyte net volume adjustment value) according to the multiple linear regression fit 621, and, at the same time, may also determine the electrolyte circulation volume adjustment value (i.e., the second electrolyte circulation volume adjustment value) and/or the electrolyte net volume adjustment value (i.e., the second electrolyte net volume adjustment value) based on at least a portion of the current electrolyte state information via the second machine learning model 622, and then the exception handling module 230 may perform a fusion process on the first electrolyte net volume adjustment value and the second electrolyte net volume adjustment value to determine a final electrolyte net volume adjustment value 632, and, similarly, perform a fusion process on the first electrolyte circulation volume adjustment value and the second electrolyte circulation volume adjustment value, to determine a final electrolyte circulation amount adjustment value 631.

In some embodiments, the fusion process may be implemented using a method such as weighted summation. For example, the first electrolyte circulation amount adjustment value and the second electrolyte circulation amount adjustment value are respectively given corresponding weight values, then the first electrolyte circulation amount adjustment value and the second electrolyte circulation amount adjustment value are respectively weighted and summed to obtain a third electrolyte circulation amount adjustment value, and the first electrolyte circulation amount adjustment value and the second electrolyte circulation amount adjustment value are weighted and summed to obtain a third electrolyte purification amount adjustment value. And taking the third electrolyte circulation amount regulating value and the third electrolyte purified water amount regulating value as final regulating values. The weight value may be determined in various ways, such as preset, set in combination with the confidence level, and the like.

In some embodiments, fusing the multiple linear regression fit 621 with the second machine learning model 622 to determine the electrolyte circulation volume adjustment value 631 and/or the electrolyte net water volume adjustment value 632 based on at least a portion 610 of the electrolyte state information may further improve the accuracy of the obtained electrolyte circulation volume adjustment value 631 and/or the electrolyte net water volume adjustment value 632.

In some embodiments, the second machine learning model 700 may include a feature extraction layer 710 and a solution determination layer 720.

In some embodiments, the feature extraction layer 710 may be used to process at least a portion of the electrolyte state information, obtaining state features 711, and the state features 711 may be used to characterize information related to the electrolyte state.

In some embodiments, at least a portion of the electrolyte state information input to the feature extraction layer 710 may be at least one of electrolyte temperature information, electrolyte flow information, current density information, electrolyte composition, electrolyte impurities, and cathode plate surface condition.

The state features 711 may be one-dimensional vectors or n-dimensional vectors. For example, the input to the feature extraction layer 710 is electrolyte flow information, and the state feature 711 output by the feature extraction layer 710 may be a one-dimensional vector for characterizing the electrolyte flow. For another example, the input to the feature extraction layer 710 is electrolyte temperature information, electrolyte flow information, and current density information, and the state feature 711 output by the feature extraction layer 710 may be a 5-dimensional vector, where 5 values in the 5-dimensional vector may be used to represent the overall electrolyte temperature, the interlayer temperature difference, the end-to-end temperature difference, the electrolyte flow, and the current density, respectively.

In some embodiments, the feature extraction layer 710 may include Convolutional Neural Networks (CNNs) such as ResNet, ResNeXt, SE-Net, DenseNet, MobileNet, ShuffleNet, RegNet, EffectientNet, or inner, or a recurrent Neural network.

In some embodiments, the scenario determination layer 720 may be used to determine an electrolyte circulation volume adjustment value and/or an electrolyte net volume adjustment value based on the status signature 711.

In some embodiments, the second machine learning model 700 may rapidly extract the state feature 711 based on at least a portion of the electrolyte state information through the feature extraction layer 710, and then determine the electrolyte circulation amount adjustment value and/or the electrolyte net water amount adjustment value based on the state feature 711 through the scheme determination layer 720, so that the determined electrolyte circulation amount adjustment value and/or the electrolyte net water amount adjustment value are more accurate, and the efficiency of adjusting the electrolyte to a normal state when the electrolyte state is abnormal is improved.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. An electrolyte anomaly monitoring method comprising:

obtaining electrolyte state information, wherein the electrolyte state information comprises at least one of electrolyte temperature information, electrolyte flow information, current density information, electrolyte components, electrolyte impurities and cathode plate surface conditions;

judging whether the state of the electrolyte is abnormal or not based on the electrolyte state information;

in response to the electrolyte condition anomaly, determining a treatment protocol.

2. The method of claim 1, the determining a processing scheme, comprising:

and determining at least one of an electrolyte circulation amount adjusting value and an electrolyte pure water amount adjusting value.

3. The method of claim 2, the determining at least one of an electrolyte circulation adjustment value and an electrolyte net water adjustment value comprising:

obtaining a corresponding relation between at least one of the electrolyte circulation amount adjusting value and the electrolyte clean water amount adjusting value and at least one part of the electrolyte state information through multiple linear regression fitting;

determining at least one of the electrolyte circulation amount adjustment value and the electrolyte clear water amount adjustment value based on at least a portion of the electrolyte state information through the correspondence.

4. The method of claim 1, the obtaining electrolyte status information comprising:

sending target position information to the unmanned aerial vehicle equipped with the detection device;

when the unmanned aerial vehicle is received at the target position, the detection device acquires the electrolyte state information.

5. An electrolyte abnormity monitoring system comprises an information acquisition module, a state judgment module and an abnormity processing module;

the information acquisition module is used for acquiring electrolyte state information, wherein the electrolyte state information comprises at least one of electrolyte temperature information, electrolyte flow information, current density information, electrolyte components, electrolyte impurities and cathode plate surface conditions;

the state judgment module is used for judging whether the state of the electrolyte is abnormal or not based on the electrolyte state information;

the abnormality processing module is used for responding to the electrolyte state abnormality and determining a processing scheme.

6. The system of claim 5, the exception handling module further to:

7. The system of claim 6, the exception handling module further to:

and determining at least one of the electrolyte circulation amount adjustment value and the electrolyte clear water amount adjustment value based on at least a part of the electrolyte state information through the correspondence.

8. The system of claim 5, the information acquisition module further to:

sending target position information to the unmanned aerial vehicle provided with the detection device;

9. An electrolyte abnormality monitoring device comprising a processor for executing the electrolyte abnormality monitoring method according to any one of claims 1 to 4.

10. A computer-readable storage medium storing computer instructions, wherein when the computer instructions in the storage medium are read by a computer, the computer executes the method for monitoring the abnormality of the electrolyte according to any one of claims 1 to 4.